Consider a consulting company which
has 120 employees. These 120 employees are composed of 60 rookies and 60
professionals. The company wishes to maintain the total number of employees at
120 so it hires a new rookie for each professional who quits. Rookies don't
quit! Professionals quit at a rate of 10 per month and it takes 6 months to
develop a professional from a rookie. Additionally, the company bills out
rookies at $10k/month and professionals at $15k/month. All 120 employees are
fully applied (I know it's a pipe dream).
An I think model for this system might look like the following:
If you run this model you find it
exists in essentially a steady state, and is about as exciting as watching
paint dry!
Now, in the 10th month the company
notices its revenue has dropped from $1.5m/month to $1.35m/month and it wonders
what has happened. And where do you think it looks for the problem? All around
the 10th month of course. And what does it find? The company finds that it
still has 120 employees, yet there are now 30 professionals and 90 rookies. A
most puzzling situation!
As it turns out, there was an organizational policy change made in month 3
which seemed to annoy professionals more than in the past, and the quit rate
jumped from 10 to 15 professionals a month. The system, with it's built in
hiring rule, essentially an auto pilot no thought action, hired one rookie for
each professional that quit. What this one time transition in quit rate
actually did was set off a 6 month transition within the organization leading
to a new equilibrium state with 30 professionals and 90 rookies. The following
graph represents this transition.
Thus, one of the real benefits of modeling and
simulation is its ability to accomplish a time and space compression between
the interrelationships within a system. This brings into view the results of
interactions that would normally escape us because they are not closely related
in time and space. Modeling and simulation can provide a way of understanding
dynamic complexity!
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